Out-of-sequence-measurement processing for probabilistic multiple hypothesis tracker with measurement reordering

In a multi-sensor central level tracking system, owing to random delay in transmission and varying preprocessing time for different sensor platforms, an earlier measurement from the same target can arrive at the fusion center after a later one. Practical data fusion schemes are challenged by the inevitable appearance of measurements that are out of sequence, called, “out-of-sequence measurements” (OOSMs). The question is how to incorporate these OOSMs in a track that has already been updated with a later observation in order to enhance the performance of the tracking system. Several approaches for a sequential algorithm to find a solution for the OOSM problem have been discussed in previous papers. An approach to address the OOSM problem in the probabilistic multi-hypothesis tracker (PMHT), being a batch algorithm, was proposed in previous paper. However, the situation of this approach was not an OOSM case but, rather, an out of sequence scan (OOSS) where a batch of data was lost and then only one scan of measurements from the lost batch arrived with the present batch. In this paper, we propose an approach that has a measurement reordering step to address the OOSM problem in the PMHT within the framework of the OOSM case and report on the performance with the simulation results. The simulation results indicate that the proposed approach may be a suitable solution for the OOSM problem in PMHT under the proper conditions of length of batch, amount of lag, density of clutter, and probability of detection for the target.     

基于词素的日文分词方法及其在OCR系统中的应用

在基于OCR技术的大规模文档录入系统中,自动检错可以大大降低人工校对成本。在日文OCR系统自动检错中,日文单词因其动词及形容词、形容动词的词尾变化现象使自动分词变得比较困难。本文提出了一种基于词素的日文分词新方法,通过建立以词素为基础单位的分词词库,以最大长度优先词条匹配方法分割出文章中有词尾变化的日文单词,避免了传统日文分词中收录单词各种词尾变化形式造成分词词库过于庞大的问题。实验表明,本文提出的分词方法可以达到99.0%的分词正确率;将该方法运用在OCR检错模块,当系统拒识率(即检错模块中认为可疑的字符在总字符中的比例)控制在1/5时,测试集上漏检率为0.05%,说明了该方法的有效性。     

基于加权二部图的汉日词对齐

高效的自动词对齐技术是词对齐语料库建设的关键所在。当前很多词对齐方法存在以下不足: 未登录词问题、灵活翻译问题和全局最优匹配问题。针对以上不足,该文提出加权二部图最大匹配词对齐模型,利用二部图为双语句对建模,利用词形、语义、词性和共现等信息计算单词间的相似度,利用加权二部图最大匹配获得最终对齐结果。在汉日词语对齐上的实验表明,该方法在一定程度上解决了以上三点不足,F-Score为80%,优于GIZA₊₊的72%。     

基于规则和统计的日语分词和词性标注的研究

日语分词和词性标注是以日语为源语言的机器翻译等自然语言处理工作的第一步。该文提出了一种基于规则和统计的日语分词和词性标注方法,使用基于单一感知器的联合分词和词性标注算法作为基本框架,在其中加入了基于规则的词语的邻接属性作为特征。在小规模测试集上的实验结果表明,这种方法分词的F值达到了98.2%,分词加词性标注的F值达到了94.8%。该文所采用的方法已经成功应用到日汉机器翻译系统中。     

利用结对编程技术改进程序设计实践教学

介绍了结对编程的概念,阐述了其作为学习编程技能的一种过程改进策略在国外计算机专业教学中的实验效果,并论述了相关影响因素。     

句法调序的统计机器翻译方法研究

为解决基于短语统计机器翻译存在的调序能力不足的问题,尝试利用句法分析器对基于短语统计机器翻译的输入汉语句子进行句法分析,然后利用转换器进行调序操作,并对部分类型短语进行预先翻译,然后再利用基于短语统计机器翻译的解码器进行翻译。重点测试了汉语中“的”字引导的复杂定语调序、介词短语、特定搭配短语、方位词短语的调序及预翻译产生的效果。实验结果表明,这些调序及预翻译操作可以显著地提高基于短语的统计机器翻译的英文译文结果的BLEU值。     

Ferret: Programming language support for multiple dynamic classification

We introduce a concept of multiple dynamic classification, a powerful generalization of single-inheritance OO, and a language Ferret which implements it. Multiple classification allows Male, Female, and Married to be subclasses of Person, arranged so that a single Person object may be both Male and Married, but may not be both Male and Female. Dynamic classification allows classes to change: a Person may acquire or lose Married status.The subclasses are true subclasses. Married carries fields (e.g., spouse) which are specific to married people. Methods may be defined on classes, and even on Boolean combinations of class: Male & Married. Ferret provides a generalization of superclass calls, so that the methods for Male &  Married can be based on those for Male and Married, without losing other classifications like Employee. Ferret has mutators, analogous to constructors but applicable when objects change class. The resulting language is powerful and highly expressive.     

多因素集对分析的系统故障模式识别方法

为研究多因素影响下系统故障模式识别,根据已有故障标准模式对故障样本模式进行分析,提出基于集对分析联系数和故障分布的系统故障模式识别新方法。根据故障背景建立故障模式识别系统,分析故障样本模式与故障标准模式,确定联系度各联系分量,计算联系度和识别度,最后通过确定故障样本模式与故障标准模式关系完成识别。对某电气系统实例分析给出了方法流程,获得了模式识别结果,从而为有针对性的采取预防和治理措施提供了决策支持。     

基于集对分析的不确定性多属性决策模型与算法

针对不确定性多属性决策问题,提出统计学中的样本特征参数均值和方差也是模糊数（区间数、三角模糊数、梯形模糊数）特征参数的理论,并用联系数A+Bi表示“均值+方差”,再根据集对分析中的联系数理论计算“均值+方差”的“模”,用这种“模”代表属性权重和属性值进行不确定性多属性决策,简便实用;利用“均值+方差”联系数中i的不同取值可以考察不确定性对排序的影响.应用实例表明该理论是有效和可行的.     

Integrating multiple document features in language models for expert finding

We argue that expert finding is sensitive to multiple document features in an organizational intranet. These document features include multiple levels of associations between experts and a query topic from sentence, paragraph, up to document levels, document authority information such as the PageRank, indegree, and URL length of documents, and internal document structures that indicate the experts’ relationship with the content of documents. Our assumption is that expert finding can largely benefit from the incorporation of these document features. However, existing language modeling approaches for expert finding have not sufficiently taken into account these document features. We propose a novel language modeling approach, which integrates multiple document features, for expert finding. Our experiments on two large scale TREC Enterprise Track datasets, i.e., the W3C and CSIRO datasets, demonstrate that the natures of the two organizational intranets and two types of expert finding tasks, i.e., key contact finding for CSIRO and knowledgeable person finding for W3C, influence the effectiveness of different document features. Our work provides insights into which document features work for certain types of expert finding tasks, and helps design expert finding strategies that are effective for different scenarios. Our main contribution is to develop an effective formal method for modeling multiple document features in expert finding, and conduct a systematic investigation of their effects. It is worth noting that our novel approach achieves better results in terms of MAP than previous language model based approaches and the best automatic runs in both the TREC2006 and TREC2007 expert search tasks, respectively.     

A pattern reordering approach based on ambiguity detection for online category learning

Pattern reordering is proposed as an alternative to sequential and batch processing for online category learning. Upon detecting that the categorization of a new input pattern is ambiguous, the input is postponed for a predefined time, after which it is reexamined and categorized for good. This approach is shown to improve the categorization performance over purely sequential processing, while yielding a shorter input response time, or latency, than batch processing. In order to examine the response time of processing schemes, the latency of a typical implementation is derived and compared to lower bounds. Gaussian and softmax models are derived from reject option theory and are considered for detecting ambiguity and triggering pattern postponement. The average latency and Rand Adjusted clustering score of reordered, sequential, and batch processing are compared through computer simulation using two unsupervised competitive learning neural networks and a radar pulse data set.     

Integration of multiple acoustic and language models for improved Hindi speech recognition system

Despite the significant progress of automatic speech recognition (ASR) in the past three decades, it could not gain the level of human performance, particularly in the adverse conditions. To improve the performance of ASR, various approaches have been studied, which differ in feature extraction method, classification method, and training algorithms. Different approaches often utilize complementary information; therefore, to use their combination can be a better option. In this paper, we have proposed a novel approach to use the best characteristics of conventional, hybrid and segmental HMM by integrating them with the help of ROVER system combination technique. In the proposed framework, three different recognizers are created and combined, each having its own feature set and classification technique. For design and development of the complete system, three separate acoustic models are used with three different feature sets and two language models. Experimental result shows that word error rate (WER) can be reduced about 4% using the proposed technique as compared to conventional methods. Various modules are implemented and tested for Hindi Language ASR, in typical field conditions as well as in noisy environment.     

基于信任关系和词相关关系的冷启动用户词特征重建

文本是社交媒体用户的重要信息之一,从文本中获取用户的词特征是实现用户主题建模、兴趣挖掘及个性化推荐等任务的基础。然而社交媒体中存在许多用户(冷启动用户)只含有少量甚至缺乏文本信息,为此该文提出一种融合用户信任关系及词相关关系的词特征重建方法。该方法通过对用户信任关系矩阵、词相关关系矩阵和用户词特征矩阵进行联合概率矩阵分解来实现对冷启动用户的词特征重建。在新浪微博和Twitter的四组数据集上的实验结果表明,该文所提出的冷启动用户词特征重建算法能够取得较好的词特征重建结果。     

Space optimal solution for data reordering in streaming applications on NoC based MPSoC

Many streaming applications feature coarse grain task farm or pipeline parallelism and can be modeled as a set of parallel threads. Performance requirements can often only be met by mapping the application onto a Multi Processor System-on-Chip (MPSoC). To avoid contention, hierarchical interconnection networks, where the central interconnect is a network-on-chip, are employed. In such a clustered MPSoC, the memory access latency varies strongly depending on the location of data, and is the principal cause of out-of-order arrival of data items.We present an algorithm which re-establishes the order of data items on the output side. If their earliness or lateness exceeds a limit previously fixed by experimentation, they are dropped, otherwise stored in a buffer. Write operations to this buffer are random access, whereas read operations are in FIFO order. Our algorithm guarantees that no data is removed from the buffer before it has been read, and, for a given throughput, minimum buffer size. The algorithm was implemented within the output co-processors for three application case studies and validated on a simulation platform based on the SoCLib library.     

An optical burst reordering model for time-based and random selection assembly strategies

Contention resolution schemes in optical burst switched networks (OBS) as well as contention avoidance schemes delay burst delivery and change the burst arrival sequence. The burst arrival sequence usually changes the packet arrival sequence and degrades the upper layer protocols performance, e.g., the throughput of the transmission control protocol (TCP).In this paper, we present and analyze a detailed burst reordering model for two widely applied burst assembly strategies: time-based and random selection. We apply the IETF reordering metrics and calculate explicitly three reordering metrics: the reordering ratio, the reordering extent metric and the TCP relevant metric. These metrics allow estimating the degree of reordering in a certain network scenario. They estimate the buffer space at the destination to resolve reordering and quantify the number of duplicate acknowledgements relevant for investigations on the transmission control protocol.We show that our model reflects the burst/packet reordering pattern of simulated OBS networks very well. Applying our model in a network emulation scenario, enables investigations on real protocol implementations in network emulation environments. It therefore serves as a substitute for extensive TCP over OBS network simulations with a focus on burst reordering.     

中文分词切分技术研究

本文分析了现有的基于词典的分词算法,在比较各种算法优缺点的基础上提出了将正向匹配算法与逆向匹配算法所得到的结果集进行叠加,生成粗分结果集的新观点,再对生成的粗分结果集构造非负权有向图,最后应用最短路径算法求解有向图。通过Nutch实验验证,该算法较Nutch原始搜索系统提高了其汉语切分的准确性以及切分速度,同时部分解决了交集型歧义切分问题。     

A visual language for modeling multiple perspectives of business process compliance rules

A fundamental challenge for enterprises is to ensure compliance of their business processes with imposed compliance rules stemming from various sources, e.g., corporate guidelines, best practices, standards, and laws. In general, a compliance rule may refer to multiple process perspectives including control flow, time, data, resources, and interactions with business partners. On one hand, compliance rules should be comprehensible for domain experts who must define, verify, and apply them. On the other, these rules should have a precise semantics to avoid ambiguities and enable their automated processing. Providing a visual language is advantageous in this context as it allows hiding formal details and offering an intuitive way of modeling the compliance rules. However, existing visual languages for compliance rule modeling have focused on the control flow perspective so far, but lack proper support for the other process perspectives. To remedy this drawback, this paper introduces the extended Compliance Rule Graph language, which enables the visual modeling of compliance rules with the support of multiple perspectives. Overall, this language will foster the modeling and verification of compliance rules in practice.